Discover the Latest in Biomedical Research Innovation

Hunted through Prism's Analyze menu for ANCOVA? It isn't there, and GraphPad's own FAQ confirms they've never shipped one. Prism can still do it, through two documented routes most users never find: the simple-linear-regression slopes test for the parallel-slopes assumption, and multiple regression with dummy-coded groups for the adjusted comparison. Here are both step by step, plus an honest comparison with describing the same analysis in plain language instead.

An audit of 200 published animal studies found only 22% had replicated the right thing, and 46% counted repeated measurements of one biological unit as independent samples. That error, pseudoreplication, inflates your n, shrinks your error bars, and manufactures significance that won't replicate. Here's what separates technical vs biological replicates, why the mistake inflates false positives, and what actually counts as a biological replicate in cell culture.

When medical residents were surveyed on interpreting a p-value, 88% were confident they understood it and 100% got it wrong. That gap is why p-value mistakes survive peer review: nobody thinks they're making one. This piece covers the five most common errors in biomedical papers, several measured at scale in real journals: the inverse probability fallacy, the replication fallacy, 'trending toward significance', p-hacking, and treating 0.05 as a bright line.

Your p-value can confirm an effect is real and say nothing about whether it's big enough to matter. Effect size answers that second question, and for a two-group comparison Cohen's d is the standard measure: the mean difference divided by the pooled standard deviation. This guide covers how to calculate and report Cohen's d, why to report it alongside every p-value and confidence interval, and how to interpret it without leaning on the 0.2/0.5/0.8 benchmarks that often don't

Struggling to choose between Violin Plots and Box Plots for your next paper? While Box Plots are the "safe" standard for summary stats, they can hide crucial biological data like bimodal populations. Violin Plots reveal the full distribution but can be misleading with small sample sizes (n<10). Our Ultimate Guide breaks down the exact protocols for pre-clinical research: learn when to use which, how to handle "dinosaur" reviewers, and why "Swarm Plots" might actually be your